Mixed Depth Object Detection

ABSTRACT

A method includes obtaining a point cloud captured by a depth sensor, and image data captured by an image sensor, the point cloud and the image data representing a support structure bearing a set of objects; obtaining an image boundary corresponding to an object from the set of objects; determining a portion of the point cloud corresponding to the image boundary; selecting, from the determined portion, a subset of points corresponding to a forward surface of the object; and generating a three-dimensional position of the object based on the forward surface.

BACKGROUND

Environments in which objects are managed, such as retail facilities,warehousing and distribution facilities, and the like, may store suchobjects in regions such as aisles of shelf modules or the like. Forexample, a retail facility may include objects such as products forpurchase, and a distribution facility may include objects such asparcels or pallets. A mobile automation apparatus may be deployed withinsuch facilities to perform tasks at various locations. For example, amobile automation apparatus may be deployed to capture data representingan aisle in a retail facility for use in detecting product statusinformation. The structure of shelves may vary along the aisle, however,which may complicate object detection and reduce the accuracy of statusinformation detected from the captured data.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed invention, and explainvarious principles and advantages of those embodiments.

FIG. 1 is a schematic of a mobile automation system.

FIG. 2 is a side view of a mobile automation apparatus in the system ofFIG. 1 .

FIG. 3 is a block diagram of certain internal components of the serverof FIG. 1 .

FIG. 4 is a diagram of a shelf module, shown in perspective and from theside.

FIG. 5 is a flowchart of a method of generating 3D positions for objectsin captured data.

FIG. 6 is a diagram illustrating data captured via an exampleperformance of block 505 of the method of FIG. 5 .

FIG. 7 is a diagram illustrating data obtained via a performance ofblock 510 of the method of FIG. 5 .

FIG. 8 is a flowchart of a method of performing block 515 of the methodof FIG. 5 .

FIG. 9 is a diagram illustrating a performance of the method of FIG. 8 .

FIG. 10 is a flowchart of a method of performing block 520 of the methodof FIG. 5 .

FIG. 11 is a diagram illustrating a performance of the method of FIG. 10.

FIG. 12 is a diagram illustrating local support structure planesobtained at block 530 of the method of FIG. 5 .

FIG. 13 is a flowchart of a method of performing block 535 of the methodof FIG. 5 .

FIG. 14 is a diagram illustrating an example performance of blocks1305-1315 of the method of FIG. 13 .

FIG. 15 is a diagram illustrating successive performances of blocks1320-1330 of the method of FIG. 13 .

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of embodiments of the present invention.

The apparatus and method components have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present invention so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

DETAILED DESCRIPTION

Examples disclosed herein are directed to a method, comprising:obtaining a point cloud captured by a depth sensor, and image datacaptured by an image sensor, the point cloud and the image datarepresenting a support structure bearing a set of objects; obtaining animage boundary corresponding to an object from the set of objects;determining a portion of the point cloud corresponding to the imageboundary; selecting, from the determined portion, a subset of pointscorresponding to a forward surface of the object; and generating athree-dimensional position of the object based on the forward surface.

Additional examples disclosed herein are directed to a method,comprising: obtaining a plurality of the three-dimensional positionsderived from images captured by an image sensor and a point cloudcaptured by a depth sensor; selecting a subset of the three-dimensionalpositions corresponding to an object; projecting the selectedthree-dimensional positions to a sequence of candidate depths;determining, at each candidate depth, a cost function associated withthe projections; and generating a combined three-dimensional position ata selected one of the candidate depths having the lowest cost function.

Further examples disclosed herein are directed to a method, comprising:obtaining (i) a point cloud, captured by a depth sensor, of a supportstructure and an obstruction, and (ii) a plurality of local supportstructure planes derived from the point cloud and corresponding torespective portions of the support structure; for each local supportstructure plane: selecting a membership set of points from the pointcloud; generating a mask based on the membership set of points;selecting a subset of points from the point cloud based on the localsupport structure plane and the mask; and detecting obstructions fromthe subset of points.

FIG. 1 depicts a mobile automation system 100 in accordance with theteachings of this disclosure. The system 100 includes a server 101 incommunication with at least one mobile automation apparatus 103 (alsoreferred to herein simply as the apparatus 103) and at least one clientcomputing device 104 via communication links 105, illustrated in thepresent example as including wireless links. In the present example, thelinks 105 are provided by a wireless local area network (WLAN) deployedvia one or more access points (not shown). In other examples, the server101, the client device 104, or both, are located remotely (i.e. outsidethe environment in which the apparatus 103 is deployed), and the links105 therefore include wide-area networks such as the Internet, mobilenetworks, and the like. The system 100 also includes a dock 106 for theapparatus 103 in the present example. The dock 106 is in communicationwith the server 101 via a link 107 that in the present example is awired link. In other examples, however, the link 107 is a wireless link.

The client computing device 104 is illustrated in FIG. 1 as a mobilecomputing device, such as a tablet, smart phone or the like. In otherexamples, the client device 104 is implemented as another type ofcomputing device, such as a desktop computer, a laptop computer, anotherserver, a kiosk, a monitor, and the like. The system 100 can include aplurality of client devices 104 in communication with the server 101 viarespective links 105.

The system 100 is deployed, in the illustrated example, in a retailfacility including a plurality of support structures such as shelfmodules 110-1, 110-2, 110-3 and so on (collectively referred to as shelfmodules 110 or shelves 110, and generically referred to as a shelfmodule 110 or shelf 110—this nomenclature is also employed for otherelements discussed herein). Each shelf module 110 supports a pluralityof products 112. Each shelf module 110 includes a shelf back 116-1,116-2, 116-3 and a support surface (e.g. support surface 117-3 asillustrated in FIG. 1 ) extending from the shelf back 116 to a shelfedge 118-1, 118-2, 118-3. A variety of other support structures may alsobe present in the facility, such as pegboards and the like.

The shelf modules 110 (also referred to as sub-regions of the facility)are typically arranged in a plurality of aisles (also referred to asregions of the facility), each of which includes a plurality of modules110 aligned end-to-end. In such arrangements, the shelf edges 118 faceinto the aisles, through which customers in the retail facility, as wellas the apparatus 103, may travel. As will be apparent from FIG. 1 , theterm “shelf edge” 118 as employed herein, which may also be referred toas the edge of a support surface (e.g., the support surfaces 117) refersto a surface bounded by adjacent surfaces having different angles ofinclination. In the example illustrated in FIG. 1 , the shelf edge 118-3is at an angle of about ninety degrees relative to the support surface117-3 and to the underside (not shown) of the support surface 117-3. Inother examples, the angles between the shelf edge 118-3 and the adjacentsurfaces, such as the support surface 117-3, is more or less than ninetydegrees.

The apparatus 103 is equipped with a plurality of navigation and datacapture sensors 108, such as image sensors (e.g. one or more digitalcameras) and depth sensors (e.g. one or more Light Detection and Ranging(LIDAR) sensors, one or more depth cameras employing structured lightpatterns, such as infrared light, or the like). The apparatus 103 isdeployed within the retail facility and, via communication with theserver 101 and use of the sensors 108, navigates autonomously orpartially autonomously along a length 119 of at least a portion of theshelves 110.

While navigating among the shelves 110, the apparatus 103 can captureimages, depth measurements and the like, representing the shelves 110(generally referred to as shelf data or captured data). Navigation maybe performed according to a frame of reference 102 established withinthe retail facility. The apparatus 103 therefore tracks its pose (i.e.location and orientation) in the frame of reference 102.

The server 101 includes a special purpose controller, such as aprocessor 120, specifically designed to control and/or assist the mobileautomation apparatus 103 to navigate the environment and to capturedata. The processor 120 is interconnected with a non-transitory computerreadable storage medium, such as a memory 122, having stored thereoncomputer readable instructions for performing various functionality,including control of the apparatus 103 to navigate the modules 110 andcapture shelf data, as well as post-processing of the shelf data. Thememory 122 can also store data for use in the above-mentioned control ofthe apparatus 103, such as a repository 123 containing a map of theretail environment and any other suitable data (e.g. operationalconstraints for use in controlling the apparatus 103, data captured bythe apparatus 103, and the like).

The memory 122 includes a combination of volatile memory (e.g. RandomAccess Memory or RAM) and non-volatile memory (e.g. read only memory orROM, Electrically Erasable Programmable Read Only Memory or EEPROM,flash memory). The processor 120 and the memory 122 each comprise one ormore integrated circuits. In some embodiments, the processor 120 isimplemented as one or more central processing units (CPUs) and/orgraphics processing units (GPUs).

The server 101 also includes a communications interface 124interconnected with the processor 120. The communications interface 124includes suitable hardware (e.g. transmitters, receivers, networkinterface controllers and the like) allowing the server 101 tocommunicate with other computing devices—particularly the apparatus 103,the client device 104 and the dock 106—via the links 105 and 107. Thelinks 105 and 107 may be direct links, or links that traverse one ormore networks, including both local and wide-area networks. The specificcomponents of the communications interface 124 are selected based on thetype of network or other links that the server 101 is required tocommunicate over. In the present example, as noted earlier, a wirelesslocal-area network is implemented within the retail facility via thedeployment of one or more wireless access points. The links 105therefore include either or both wireless links between the apparatus103 and the mobile device 104 and the above-mentioned access points, anda wired link (e.g. an Ethernet-based link) between the server 101 andthe access point.

The processor 120 can therefore obtain data captured by the apparatus103 via the communications interface 124 for storage (e.g. in therepository 123) and subsequent processing (e.g. to detect objects suchas shelved products in the captured data, and detect status informationcorresponding to the objects). The server 101 maintains, in the memory122, an application 125 executable by the processor 120 to perform suchsubsequent processing. In particular, as discussed in greater detailbelow, the server 101 is configured, via execution of the instructionsof the application 125 by the processor 120, to determinethree-dimensional positions (e.g. in the frame of reference 102) forvarious objects detected from the data captured by the apparatus 103.

The server 101 may also transmit status notifications (e.g.notifications indicating that products are out-of-stock, in low stock ormisplaced) to the client device 104 responsive to the determination ofproduct status data. The client device 104 includes one or morecontrollers (e.g. central processing units (CPUs) and/orfield-programmable gate arrays (FPGAs) and the like) configured toprocess (e.g. to display) notifications received from the server 101.

Turning now to FIG. 2 , the mobile automation apparatus 103 is shown ingreater detail. The apparatus 103 includes a chassis 201 containing alocomotive assembly 203 (e.g. one or more electrical motors drivingwheels, tracks or the like). The apparatus 103 further includes a sensormast 205 supported on the chassis 201 and, in the present example,extending upwards (e.g., substantially vertically) from the chassis 201.The mast 205 supports the sensors 108 mentioned earlier. In particular,the sensors 108 include at least one imaging sensor 207, such as adigital camera. In the present example, the mast 205 supports sevendigital cameras 207-1 through 207-7 oriented to face the shelves 110.

The mast 205 also supports at least one depth sensor 209, such as a 3Ddigital camera capable of capturing both depth data and image data. Theapparatus 103 also includes additional depth sensors, such as LIDARsensors 211. In the present example, the mast 205 supports two LIDARsensors 211-1 and 211-2. As shown in FIG. 2 , the cameras 207 and theLIDAR sensors 211 are arranged on one side of the mast 205, while thedepth sensor 209 is arranged on a front of the mast 205. That is, thedepth sensor 209 is forward-facing (i.e. captures data in the directionof travel of the apparatus 103), while the cameras 207 and LIDAR sensors211 are side-facing (i.e. capture data alongside the apparatus 103, in adirection perpendicular to the direction of travel). In other examples,the apparatus 103 includes additional sensors, such as one or more RFIDreaders, temperature sensors, and the like.

The mast 205 also supports a plurality of illumination assemblies 213,configured to illuminate the fields of view of the respective cameras207. The illumination assemblies 213 may be referred to collectively asan illumination subsystem. That is, the illumination assembly 213-1illuminates the field of view of the camera 207-1, and so on. Thecameras 207 and lidars 211 are oriented on the mast 205 such that thefields of view of the sensors each face a shelf 110 along the length 119of which the apparatus 103 is traveling. The apparatus 103 is configuredto track a pose of the apparatus 103 (e.g. a location and orientation ofthe center of the chassis 201) in the frame of reference 102, permittingdata captured by the apparatus 103 to be registered to the frame ofreference 102 for subsequent processing.

Turning to FIG. 3 , certain components of the application 125 areillustrated. As will be apparent to those skilled in the art, theapplication 125 can also be implemented as a suite of distinctapplications in other examples. Further, some or all of the modulesdescribed below can be implemented via distinct control hardware such asone or more ASICs and/or FPGAs.

The application 125 includes a three-dimensional position generator 304that is configured to obtain positions of detected objects (e.g.products on the shelves 110, product labels on shelf edges) in twodimensions, such as positions within 2D images captured by the apparatus103. Another component of the server 101 or a separate computing devicecan be responsible for the detection of objects in the images andprovision of the 2D positions to the application 125. Having obtainedthe 2D positions, as well as point cloud data corresponding to theshelves 110 where the objects were detected, the generator 304 isconfigured to identify which points in the point cloud represent theobjects based on the 2D positions. In other words, the generator 304 isconfigured to project the 2D image-based positions into the point cloud.

The application 125 also includes an obstruction detector 308. Thedetector 308 is configured to obtain point cloud data captured by theapparatus 103 depicting shelves 110, and to detect irregular objectsfrom the point cloud data. Irregular objects, also referred to herein asobstructions, include objects that may not be readily detectable by theprocesses used to detect 2D positions of objects from images (which maythen be used by the generator 304). Examples of obstructions includeclip strips, which may hold coupons, samples or the like, and extendinto the aisle from the front of a shelf module 110.

Each object detected from data captured by the apparatus 103 my appearin multiple captures. That is, each product label disposed on a shelfedge 118, and each clip strip or other obstruction, may appear inmultiple image frames and/or point clouds, because the apparatus 103 maycapture a sequence of images and point clouds as it traverses an aisle.The application 125 therefore also includes a cluster generator 312 thatis configured to accept 3D positions of objects from the generator 304and/or the detector 308, and to cluster such positions to yield asmaller set of positions each corresponding to a unique object. Theoutput of the cluster generator 312 can be used to generate productstatus data and the like by a downstream process at the server 101 oranother computing device.

FIG. 4 illustrates a module 410 including three shelves. As discussed inconnection with the modules 110 in FIG. 1 , the shelves of the module410 includes support surfaces 417-1, 417-2 and 417-3 extending from ashelf back 416 to respective shelf edges 418-1, 418-2 and 418-3. Theshelf edge 418-3 supports two labels 420-1 and 420-2, corresponding toproducts 412-1 and 412-2, respectively. The shelf edge 418-2, meanwhile,supports a label 420-3 corresponding to a product 412-3. The shelf edge418-1 as shown does not support any products, but does supportobstructions such as clip strips 424-1 and 424-2, which hang from theshelf edge 418-1 down to the shelf edge 412-2.

FIG. 4 also includes a side view of the module 410, showing that theshelf edges 418-1 and 418-2 are at a different depth (i.e. positionalong the Y axis of the frame of reference 102) than the shelf edge418-3. In particular, the shelf edges 418-1 and 418-2 have a depth of428-1 as measured from the back 416, while the shelf edge 418-3 has agreater depth 428-2.

The different depths of the shelf edges 418 can negatively affect theaccuracy of certain mechanisms for detecting objects such as the labels420 and products 412. For example, some mechanisms accept as input asingle vertical (i.e. aligned with the XZ plane of the frame ofreference 102) shelf plane containing the shelf edges 418.Two-dimensional positions of objects such as the labels 420, acquired bydetection from images captured by the apparatus 103, can be employed todetermine 3D positions of the labels 420 by projecting such 2D positionsonto the shelf plane. When no single shelf plane accurately defines thepositions of the shelf edges 418, however, the above mechanism mayproduce inaccurate 3D positions for the labels 420. Inaccuratepositioning of detected objects can also lead to incorrect detection ofmultiple objects where in reality there is only one.

Further, some mechanisms employed to detect obstructions such as theclip strips 424 employ a shelf plane as mentioned above, to partition acaptured point cloud into points in front of the shelf plane (i.e. inthe aisle) and points behind the shelf plane (i.e. over the supportsurfaces 417). The points in the aisle may then be evaluated accordingto various criteria to detect obstructions (as opposed to noise orproducts 412 sticking off the shelves). However, in modules such as themodule 410, the mixed depth of the shelf edges 418 renders the use of asingle shelf plane as described above impractical.

The server 101 is therefore configured, as described below in greaterdetail, to implement mechanisms for determining 3D positions ofimage-detected objects such as the labels 420, and for determining 3Dpositions of point-cloud detected obstructions such as the clip strips424, in a manner that is robust to the presence of mixed depth shelfedges 418.

FIG. 5 shows a flowchart of a method 500 of obtaining 3D positions forobjects from captured data representing support structures such as themodule 410. Although the method 500 can be implemented to detect objectsfor a wide variety of support structures, including those with uniformshelf depth, the method 500 will be discussed below in conjunction withits performance to detect objects on a support structure with mixedshelf depth, such as the module 410. The method 500 as described belowis performed by the server 101, and in particular by the application125. In other examples, however, at least some of the functionalityimplemented via the method 500 can be performed by another computingdevice, such as the apparatus 103.

At block 505, the server 101 is configured to obtain image and depthdata (i.e. one or more point clouds) depicting a support structure. Theimage data may include a plurality of 2D images previously captured bythe apparatus 103, e.g. while traversing an aisle including supportstructures such as the module 410. The point cloud includes a pluralityof points with coordinates defined in three dimensions, e.g. accordingto the frame of reference 102, captured by the apparatus 103 during theabove-mentioned traversal of the support structures. A plurality ofindividual point clouds can be obtained at block 505, however in thediscussion below a single point cloud will be discussed for clarity ofillustration. The single point cloud can be produced from multipleindividual point cloud captures by the apparatus 103. The images andpoint cloud obtained at block 505 may be retrieved from the repository123, for example.

FIG. 6 illustrates an example point cloud 600 and an example set 604 ofimages obtained at block 505. As is evident from FIG. 6 , the pointcloud 600 depicts the module 410. The labels 420 are not shown in thepoint cloud 600, because they are coplanar with the shelf edges 418 inthis example, and therefore may not be distinguishable from the shelfedges 418 from the point cloud 600 alone. The set 604 of images alsodepict the module 410, with each image corresponding to a particularportion of the module 410 as the apparatus traversed the length of themodule 410. An example portion 608 corresponding to the first image inthe set 604 is illustrated. As seen in FIG. 6 , the set 604 of imagesoverlap, such that each object (e.g. clip strips 424, products 412) areshown in more than one image.

Returning to FIG. 5 , following acquisition of the images and pointcloud, the server 101 is configured to perform two branches offunctionality to determine 3D positions for objects. One branch isperformed to determine the 3D positions of objects such as the labels420 that are initially detected from 2D images. The other branch isperformed to detect objects that are difficult to detect from 2D images,including obstructions such as the clip strips 424.

Beginning with the generation of 3D positions for image-detectedobjects, at block 510 the server 101 is configured to obtain objectboundaries detected from the set 604 of images mentioned above. Eachobject boundary obtained at block 510, in other words, is atwo-dimensional boundary such as a bounding box indicating a portion ofan images in the set 604 where a given object has been detected. Theboundary may therefore also be referred to as an image boundary (havingbeen derived from image data, rather than from point cloud data). Thedetection of objects from the images can be performed according to anysuitable object detection mechanism, which need not be implementedwithin the application 125. That is, object detection from the imagesobtained at block 505 is performed separately from the method 500, byanother application at the server 101, or by another computing device.Examples of such detection mechanisms include feature recognitionalgorithms, machine learning-based object detection, and the like.

The object boundaries obtained at block 510 indicate the position ofobjects in two dimensions, e.g. along the X and Z axes of the frame ofreference 102. However, the object boundaries do not indicate the depth(along the Y axis of the frame of reference 102) of the objects. Turningbriefly to FIG. 7 , an example image 700 is shown, depicting the labels420-1, 420-2 and 420-3. FIG. 7 also illustrates boundaries 704-1, 704-2and 704-3 corresponding to the labels 420, as detected from the image700. The boundaries 704 are defined as bounding boxes (e.g. coordinatesfor each corner of the boundary 704 along the X and Z axes of the frameof reference 102). As will be apparent, the boundaries obtained at block510 may include boundaries for other objects, as well as additionaldetected boundaries for the labels 420 from other images. That is, theboundaries obtained at block 510 may include a plurality of boundariesfor each detected object.

At block 515, the server 101, and particularly the 3D position generator304, is configured to convert the 2D positions detected from images into3D positions in the frame of reference 102. In general, the server 101generates 3D positions from a given 2D boundary obtained at block 510 bydetermining a portion of the point cloud obtained at block 505 thatcorresponds to the boundary (i.e. that could contain the objectidentified by the boundary), and then by identifying a surface withinthat portion of the point cloud that is likely to correspond to theobject.

FIG. 8 illustrates a method 800 of performing the generation of 3Dpositions at block 515. At block 805, the server 101 selects the nextboundary for processing. In the present example, the boundary 704-3 isselected for processing. At block 810, the server 101 determines avolume of the point cloud from block 505 that corresponds to theselected boundary. As will be apparent to those skilled in the art,although the depth at which the object corresponding to each boundary704 resides is unknown, the boundary 704 nevertheless constrains thepossible positions of the object within the point cloud.

Turning to FIG. 9 , an image sensor 207 is shown, along with theboundary of the image 700. The image 700 represents the full extent of afield of view of the image sensor 207, whose position relative to thepoint cloud is known (e.g. because the tracked pose of the apparatus103, mentioned earlier, is stored in conjunction with the image 700).The position and size of the boundary 704-3 within the image 700indicates which portion of the field of view of the image sensor 207captured the pixels within the boundary 704-3. Based on the knownlocation of the image sensor 207 relative to the point cloud 600,therefore, and based on operational parameters of the image sensor 207that define the size and shape of the image sensor field of view, theserver 101 determines a portion 900 of the field of view thatcorresponds to the boundary 704-3. That is, regardless of the depth ofthe object represented by the boundary 704-3, any points representingthe object are within the portion 900. The portion 900 can be defined,for example, by a set of planes defining a pyramid-shape region in thepoint cloud 600.

Having defined the volume 900 corresponding to the boundary 704-3, atblock 815 the server 101 is configured to select a subset of points fromthe volume 900 that correspond to a forward surface of the object. Aswill be apparent to those skilled in the art, the volume 900 may containpoints that do not correspond to the relevant object (e.g. the label420-3 in the present example). For example, the boundary 704-3 may notcorrespond exactly to the actual edges of the label 420-3. To identifywhich points within the volume 900 are likely to correspond to the label420-3, the server 101 is configured to identify the closest group ofpoints in the volume 900 to the image sensor 207 (i.e. along the Y axisof the frame of reference 102).

Turning again to FIG. 9 , a group 904 of points from the point cloud 600that fall within the volume 900 are shown, along with the actualposition of the label 420-3. The group 904 thus includes points thatcorrespond to the label 420-3, but also includes points that correspondto the support surface 417-2, capture noise, or the like. FIG. 9 alsoillustrates the group 904 from the side, from which it can be seen thata majority of the points in the group 904 have similar depths, likelyindicating the presence of a contiguous surface (i.e. the label 420-3).To identify such a surface, the server 101 can be configured to generatea histogram 908 in which each bin corresponds to a given depth rangealong the Y axis. The value of each bin indicates how many points fromthe group 904 fall within the corresponding depth range. The server 101can then select the bin having the highest value, and select the depthcorresponding to that bin (e.g. the average of the depth rangerepresented by the bin) as the depth for the object under consideration.

In the present example, therefore, the server 101 selects the bin 912 atblock 815, and assigns the depth of the bin 912 to the boundary 704-3.That is, the depth of the bin 912 is selected to represent the forwardsurface of the label 420-3. Returning to FIG. 8 , at block 820 theserver 101 is configured to generate a 3D position for the boundary704-3 by projecting the boundary 704-3 to the depth selected at block815. Such a projection places the boundary 704-3 at the selected depthwithin the volume 900, and therefore determines the coordinates, inthree dimensions according to the frame of reference 102, of theboundary 704-3.

At block 825, the server 101 returns to block 805 if boundaries 704remain to be processed, or proceeds to block 520 if all boundaries 704have been processed to determine their 3D positions.

Returning to FIG. 5 , at block 520 the server 101 is configured togenerate combined 3D positions for each detected object. The performanceof block 520 may also be referred to as clustering the 3D positions fromblock 515. As noted earlier, each object in the module 400 is likely tobe shown in more than one image in the set 604, and therefore more thanone boundary is likely to be generated for the object, and converted toa 3D boundary at block 515. At block 520, the server 101 identifies setsof 3D positions that are likely to correspond to the same object, andgenerates a single combined position for the object.

Turning to FIG. 10 , a method 1000 of generating combined 3D positionsat block 520 is shown. The method 1000 is performed, in the presentexample, by the cluster generator 312. At block 1005, having obtained(via the performance of block 515) 3D positions for each object boundaryreceived at block 510, the server 101 is configured to select a subsetof 3D positions for processing. The subset of 3D positions selected atblock 1005 corresponds to a single physical object. The selection of thesubset can be based on metadata or other properties stored inconjunction with the boundaries obtained at block 510. For example, inthe case of boundaries representing labels 420, the boundaries may beobtained along with data decoded from label barcodes identified in theimage set 604. Thus, a subset of 3D positions generated from boundariesassociated with the same barcode data may be selected at block 1005.

FIG. 11 illustrates a subset of 3D positions 1100-1, 1100-2 and 1100-3all associated with the same barcode data and therefore assumed tocorrespond to the same single label 420 (e.g. the label 420-3). The 3Dpositions 1100 are also shown from the front and from the side toillustrate the differences in position and depth between each 3Dposition 1100. That is, the position derived from each image-baseddetection of the label 420-3 may not be entirely consistent with theother positions derived from other images also showing the label 420-3.The server 101 therefore, via the method 1000, identifies a single 3Dposition likely to accurately represent the true location of the label420-3.

At block 1010 the server 101 is configured to project each of thepositions 1100 to the first of a sequence of candidate depths. In thepresent example, the candidate depths are the depths of the positions1100 themselves. Thus, at block 1010 the server 101 projects each of thepositions 1100 to the depth of the forward-most position (e.g. theposition 1100-1). The resulting projection for the position 1100-1 willbe unchanged, but the position along the X and Z axes, as well as thesize, of the positions 1100-2 and 1100-3 will be modified by theprojection.

At block 1015, the server 101 is configured to determine a cost functionrepresenting a degree of agreement between the projections from block1010. When, at block 1020, the server 1010 determines that the costfunction is lower than in the previous iteration of block 1015, the nextcandidate depth is selected and blocks 1015 and 1020 are repeated. Whenthe cost function does not decrease between iterations, the most recentcandidate depth processed is employed to generate the combined position,at block 1025.

Referring again to FIG. 11 , two example performances of blocks1010-1020 are shown. In particular, at block 1010 the server 101generates a first set of projections 1104-1, 1104-2 and 1104-3 at thedepth of the 3D position 1100-1. The projection 1104-1 is thereforeidentical to the 3D position 1100-1, but the projections 1104-2 and1104-3 are not identical to the 3D positions 1100-2 and 1100-3. The costfunction determined at block 1015 may be, for example, the sum ofdistances between the centroids of the projections 1104 (that is, a sumof three distances). Following the first performance of block 1015 thedetermination at block 1020 is automatically affirmative.

FIG. 11 illustrates a second performance of block 1010, at which thepositions 1100 are projected to the depth of the 3D position 1100-3. Thesecond performance of block 1010 yields a set of projections 1108-1,1108-2, and 1108-3. The cost function is recomputed at block 1015. Asshown in FIG. 11 , the centroids of the projections 1108 are separatedby smaller distances than the centroids of the projections 1104. Thedetermination at block 1020 is therefore affirmative, and blocks 1010,1015 and 1020 are therefore repeated, projecting the positions 1100 tothe depth of the position 1100-2. It is assumed, for illustrativepurposes, that the centroids of the resulting projections from thisthird performance of block 1010 are at greater distances from oneanother than for the projections 1108. The determination at block 1020is therefore negative, and the server 101 therefore proceeds to block1025.

At block 1025, the server 101 generates a combined 3D position at thecandidate depth with the lowest cost function (i.e. the depth of the 3Dposition 1100-3 in this example). The server 101 may, for example,determine an average position of the three projections 1108, e.g. byaveraging the XZ coordinates of the corners of the projections 1108, togenerate a single XZ coordinate for each corner of a combined position1112. The depth (i.e. the Y coordinate) of the combined position 1112can be equal to the depth of the position 1100-3. Following generationof the combined position 1112 at block 1025, the 3D positions 1100 maybe discarded.

At block 1030, the server 101 determines whether any subsets of 3Dpositions remain to be processed. If the determination at block 1030 isaffirmative, the server 101 returns to block 1005. Otherwise, the server101 proceeds to block 525 of the method 500. At block 525, the server101 is configured to present the 3D position(s) generated at block 520.The positions can be presented by rendering on a display, transmittingto another computing device such as the client device 105, passing toanother application at the server 101 (e.g. to generate product statusdata), or the like.

Returning to FIG. 5 , the second branch of processing for detectingobstructions such as the clip strips 424 will now be discussed,beginning at block 530. As noted earlier, the clip strips 424 and otherobstructions may be difficult to detect from 2D images, and the server101 is therefore configured to detect such objects directly from thepoint cloud 600.

At block 530 the server 101 (specifically, the obstruction detector 308)obtains one or more local support structure planes. The detection of thelocal support structure planes is performed by another application atthe server 101, or another computing device, and is therefore notdiscussed in greater detail herein. Turning to FIG. 12 , two examplelocal support structure planes 1200 and 1204 are shown, eachcorresponding to a portion of the point cloud 600. In particular, theplane 1200 is at the depth of the shelf edge 418-3, while the plane 1204is at the depth of the shelf edges 418-1 and 418-2. The planes 1200 and1204 thus not only have different depths, but also have differentextents along the X and Z axes in the frame of reference 102.

At block 535, the server 101 is configured to detect obstructions basedon each of the planes obtained at block 530. Turning to FIG. 13 , amethod 1300 of detecting obstructions at block 535 is illustrated. Atblock 1305, the server 101 selects a plane for processing. In thepresent example, it will be assumed that the plane 1204 is selected atblock 1305.

At block 1310, the server 101 is configured to select a subset of thepoints in the point cloud 600 that are considered members of the planefrom block 1305, and to generate a membership map, or mask, based on theselected members. Member points are those with X and Z coordinateswithin the bounds of the selected plane, and with depths (i.e. along theY axis) within a threshold of the depth of the selected plane. Thethreshold is selected to encompass a typical range of obstructiondepths, e.g. between about 5 and about 10 centimeters on either side ofthe selected plane. In some examples, the threshold can be different oneither side of the plane (e.g. about 10 cm into the aisle from theplane, and about 2 cm behind the plane).

Thus, in the present example, the member points selected at block 1310include those defining the shelf edges 418-1 and 418-2, as well as thepoints defining the forward surface of the product 412-3, the label420-3 and the clip strips 424. However, the members do not include anypoints defining the product 412-1, even though at least some of thosepoints may be within the depth threshold of the plane 1204 (because anypoints defining the product 412-1 are outside the X and Z bounds of theplane 1204.

To generate the mask, the server 101 projects all of the selected memberpoints to the depth of the plane 1204. Optionally, the server 101 mayperform a morphological operation such as dilation and/or erosion tofill gaps between the points. FIG. 14 illustrates an example mask 1400,in which the white portions (which may be referred to as a selectionregion) correspond to the product 412-3 and the clip strips 424, as wellas the shelf edges 418-1 and 418-2. The shaded portions indicate regionsthat will not be inspected for detecting obstructions, as set out below.

Having generated the mask at block 1310, the server 101 is configured todetect obstructions based on both the plane 1204 (or more generally, theplane selected at block 1305) and the mask 1400. At block 1315, theserver 101 sets a selection depth according to a coarse interval.Specifically, the selection depth set at block 1315 is set bydecrementing the depth of the plane 1204 by the coarse interval. Anexample performance of block 1315 is illustrated at FIG. 14 .Specifically, a coarse interval 1404 is illustrated, and a selectiondepth 1408 is defined as a plane parallel to the plane 1204 and locatedat a depth that is shifted forward (into the aisle) from the plane 1204by the coarse interval 1404. Any points in front of the selection depth1408 are selected in the subset at block 1315. A variety of coarseintervals can be employed, for example depending on the expected size ofthe obstructions. In the present example, the coarse interval is about 6cm, although other coarse intervals smaller than, or larger than, 6 cmmay be employed in other embodiments.

At block 1320, the points of the selected subset are projected to theselection depth 1408, but the mask 1400 is applied to the projection,such that any projected points outside the white portions of the mask1400 are discarded. That is, although at least a portion of the products412-1 and 412-2, as well as the shelf edge 418-3, are in front of theselection depth 1408, points defining those objects are omitted from theprojection because they fall outside the white portion of the mask 1400.

The projection resulting from block 1320 is processed to detectobstruction candidates therein. For example, blob detection or the likecan be performed to detect contiguous regions in the projection that maycorrespond to objects such as the clip strips 424. When such regions aredetected, they may be compared to various criteria, such as a minimumsize (e.g. area), and a number of detections. If a region exceeds aminimum size it may be retained for further processing, otherwise theregion may be discarded.

At block 1325, the server 101 determines whether additional selectiondepths remain to be processed. The server 101 is configured to process apredefined set of selection depths, from the initial selection depth1408 to a final selection depth, which maybe behind the plane 1204. Whenthe determination at block 1325 is affirmative, the server 101 isconfigured to expand the selected subset of points by a fine interval.Specifically, the server 101 is configured to shift the selection depthbackwards (away from the aisle) by a smaller interval than the coarseinterval (e.g. about 1 cm). The server 101 is then configured to repeatthe performance of block 1320 for the new selection depth, which willnow capture a greater number of points than the initial selection depth1408.

Turning to FIG. 15 , a set of projections 1500, 1504 and 1508 generatedat successive performances of block 1320 are shown. As seen in FIG. 15 ,successively greater selection depths (i.e. closer to the shelf back416) capture successively greater portions of the point cloud 600, butonly points within the bounds of the mask 1400 are considered. Theprojection 1500 contains no points, while the projection 1504 containsportions of the clip strips 424, and the projection 1508 containsfurther portions of the clip strips 424, as well as a portion of theproduct 412-3.

An obstruction is detected when the selection depths have beenexhausted, and the obstruction is detected in the same region of theprojections for at least a threshold number of projections (e.g. two).For each detected obstruction, the server 101 may generate athree-dimensional bounding box fitted to the points that contributed tothe detection. Thus, the server 101 may generate a bounding box fittedto the points corresponding to each of the clip strips 424 asrepresented in the projections 1500, 1504, and 1508.

The server 1325, when all selection depths have been processed, returnsany detected obstructions at block 1335. The above process is thenrepeated for any remaining planes (e.g. the plane 1200). When no planesremain to be processed, the server 101 returns to block 525 of themethod 500, as described earlier.

In the foregoing specification, specific embodiments have beendescribed. However, one of ordinary skill in the art appreciates thatvarious modifications and changes can be made without departing from thescope of the invention as set forth in the claims below. Accordingly,the specification and figures are to be regarded in an illustrativerather than a restrictive sense, and all such modifications are intendedto be included within the scope of present teachings.

The benefits, advantages, solutions to problems, and any element(s) thatmay cause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeatures or elements of any or all the claims. The invention is definedsolely by the appended claims including any amendments made during thependency of this application and all equivalents of those claims asissued.

Moreover in this document, relational terms such as first and second,top and bottom, and the like may be used solely to distinguish oneentity or action from another entity or action without necessarilyrequiring or implying any actual such relationship or order between suchentities or actions. The terms “comprises,” “comprising,” “has”,“having,” “includes”, “including,” “contains”, “containing” or any othervariation thereof, are intended to cover a non-exclusive inclusion, suchthat a process, method, article, or apparatus that comprises, has,includes, contains a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus. An element proceeded by“comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . .a” does not, without more constraints, preclude the existence ofadditional identical elements in the process, method, article, orapparatus that comprises, has, includes, contains the element. The terms“a” and “an” are defined as one or more unless explicitly statedotherwise herein. The terms “substantially”, “essentially”,“approximately”, “about” or any other version thereof, are defined asbeing close to as understood by one of ordinary skill in the art, and inone non-limiting embodiment the term is defined to be within 10%, inanother embodiment within 5%, in another embodiment within 1% and inanother embodiment within 0.5%. The term “coupled” as used herein isdefined as connected, although not necessarily directly and notnecessarily mechanically. A device or structure that is “configured” ina certain way is configured in at least that way, but may also beconfigured in ways that are not listed.

It will be appreciated that some embodiments may be comprised of one ormore specialized processors (or “processing devices”) such asmicroprocessors, digital signal processors, customized processors andfield programmable gate arrays (FPGAs) and unique stored programinstructions (including both software and firmware) that control the oneor more processors to implement, in conjunction with certainnon-processor circuits, some, most, or all of the functions of themethod and/or apparatus described herein. Alternatively, some or allfunctions could be implemented by a state machine that has no storedprogram instructions, or in one or more application specific integratedcircuits (ASICs), in which each function or some combinations of certainof the functions are implemented as custom logic. Of course, acombination of the two approaches could be used.

Moreover, an embodiment can be implemented as a computer-readablestorage medium having computer readable code stored thereon forprogramming a computer (e.g., comprising a processor) to perform amethod as described and claimed herein. Examples of suchcomputer-readable storage mediums include, but are not limited to, ahard disk, a CD-ROM, an optical storage device, a magnetic storagedevice, a ROM (Read Only Memory), a PROM (Programmable Read OnlyMemory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM(Electrically Erasable Programmable Read Only Memory) and a Flashmemory. Further, it is expected that one of ordinary skill,notwithstanding possibly significant effort and many design choicesmotivated by, for example, available time, current technology, andeconomic considerations, when guided by the concepts and principlesdisclosed herein will be readily capable of generating such softwareinstructions and programs and ICs with minimal experimentation.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

1. A method, comprising: obtaining a plurality of the three-dimensionalpositions derived from images captured by an image sensor and a pointcloud captured by a depth sensor; selecting a subset of thethree-dimensional positions corresponding to an object; projecting theselected three-dimensional positions to a sequence of candidate depths;determining, at each candidate depth, a cost function associated withthe projections; and generating a combined three-dimensional position ata selected one of the candidate depths having a lowest one of the costfunctions determined at each candidate depth.
 2. The method of claim 1,further comprising: determining that the three-dimensional positionscorrespond to the object by comparing item identifiers associated withthe three-dimensional positions.
 3. The method of claim 1, wherein thecost function includes a sum of distances between the projections. 4.The method of claim 1, wherein generating the combined three-dimensionalposition includes determining an average of the projections to theselected candidate depth.
 5. The method of claim 1, wherein obtainingeach of the three-dimensional positions includes: obtaining a pointcloud and image data representing a support structure bearing theobject; obtaining an image boundary corresponding to the object;determining a portion of the point cloud corresponding to the imageboundary; selecting, from the determined portion, a subset of pointscorresponding to a forward surface of the object; and generating thethree-dimensional position based on the forward surface
 6. The method ofclaim 1 wherein the candidate depths include depths corresponding toeach of the three-dimensional positions.
 7. A method, comprising:obtaining (i) a point cloud, captured by a depth sensor, of a supportstructure and an obstruction, and (ii) a plurality of local supportstructure planes derived from the point cloud and corresponding torespective portions of the support structure; for each local supportstructure plane: selecting a membership set of points from the pointcloud; generating a mask based on the membership set of points;selecting a subset of points from the point cloud based on the localsupport structure plane and the mask; and detecting obstructions fromthe subset of points.
 8. The method of claim 7, wherein generating themask comprises: projecting the membership set of points to the depth ofthe local support structure plane.
 9. The method of claim 8, whereinselecting the subset of points includes: identifying points having adepth smaller than a selection depth; and selecting, from the identifiedpoints, the subset of points having locations within a selection regionof the mask.
 10. The method of claim 7, wherein selecting the membershipset of points includes selecting points having depths within a thresholdof the local support structure plane.
 11. The method of claim 10,wherein selecting the membership set of points further includesselecting points located within a boundary defined by the local supportstructure plane.